In [2]:
import numpy as np
import pandas as pd
import cv2
from matplotlib import pyplot as plt
import matplotlib.cm as cm
import matplotlib.gridspec as gridspec
from pylab import imread
from skimage.color import rgb2gray
In [3]:
def ShowImage(ImageList, nRows = 1, nCols = 2, WidthSpace = 0.00, HeightSpace = 0.00):
from matplotlib import pyplot as plt
import matplotlib.gridspec as gridspec
gs = gridspec.GridSpec(nRows, nCols)
gs.update(wspace=WidthSpace, hspace=HeightSpace) # set the spacing between axes.
plt.figure(figsize=(20,10))
for i in range(len(ImageList)):
ax1 = plt.subplot(gs[i])
ax1.set_xticklabels([])
ax1.set_yticklabels([])
ax1.set_aspect('equal')
plt.subplot(nRows, nCols,i+1)
image = ImageList[i].copy()
if (len(image.shape) < 3):
plt.imshow(image, plt.cm.gray)
else:
plt.imshow(image)
plt.title("Image " + str(i))
plt.axis('off')
plt.show()
In [4]:
def InfoImage(IM):
Image = IM
Width = Image.shape[1]
Height = Image.shape[0]
Channel = len(Image.shape)
print("Width : ", Width, " Height : ", Height, " Channel : ", Channel)
if(Channel == 2):
print("Min Intensity: ", IM.min(), " Max Intensity: ", IM.max())
else:
print("Red - Min Intensity: ", IM[:,:,0].min(), " Max Intensity: ", IM[:,:,0].max())
print("Green - Min Intensity: ", IM[:,:,1].min(), " Max Intensity: ", IM[:,:,1].max())
print("Blue - Min Intensity: ", IM[:,:,2].min(), " Max Intensity: ", IM[:,:,2].max())
In [72]:
def ResizeImage(IM, DesiredWidth, DesiredHeight):
from skimage.transform import rescale, resize
OrigWidth = float(IM.shape[1])
OrigHeight = float(IM.shape[0])
Width = DesiredWidth
Height = DesiredHeight
if((Width == 0) & (Height == 0)):
return IM
if(Width == 0):
Width = int((OrigWidth * Height)/OrigHeight)
if(Height == 0):
Height = int((OrigHeight * Width)/OrigWidth)
dim = (Width, Height)
# print(dim)
resizedIM = cv2.resize(IM, dim, interpolation = cv2.INTER_NEAREST)
# imshows([IM, resizedIM], ["Image", "resizedIM"],1,2)
return resizedIM
In [5]:
# Read Image
image_color = imread("Sample07/giraffe.jpg")
# Convert Image into Gray
image_gray = cv2.cvtColor(image_color, cv2.COLOR_RGB2GRAY)
# Display Image
ShowImage([image_color, image_gray], 1, 2)
In [6]:
# Scaling
image_scale01 = cv2.resize(image_color,None,fx=0.5, fy=1, interpolation = cv2.INTER_CUBIC)
image_scale02 = cv2.resize(image_color,None,fx=0.5, fy=0.5, interpolation = cv2.INTER_CUBIC)
image_scale03 = cv2.resize(image_color,None,fx=1, fy=0.5, interpolation = cv2.INTER_CUBIC)
print("\n image_color :")
InfoImage(image_color)
print("\n image_scale01 :")
InfoImage(image_scale01)
print("\n image_scale02 :")
InfoImage(image_scale02)
print("\n image_scale03 :")
InfoImage(image_scale03)
ShowImage([image_scale01, image_scale02, image_scale03], 1, 3)
In [7]:
# Translation
rows,cols = image_color.shape[:2]
TranslationMatrix = np.float32([[1,0,200],[0,1,0]])
image_translation01 = cv2.warpAffine(image_color,TranslationMatrix,(cols,rows))
TranslationMatrix = np.float32([[1,0,0],[0,1,200]])
image_translation02 = cv2.warpAffine(image_color,TranslationMatrix,(cols,rows))
TranslationMatrix = np.float32([[1,0,200],[0,1,200]])
image_translation03 = cv2.warpAffine(image_color,TranslationMatrix,(cols,rows))
TranslationMatrix = np.float32([[1,0,-200],[0,1,-200]])
image_translation04 = cv2.warpAffine(image_color,TranslationMatrix,(cols,rows))
In [8]:
ShowImage([image_translation01, image_translation02], 1, 2)
In [9]:
ShowImage([image_translation03, image_translation04], 1, 2)
In [10]:
# Read Image
image_color = imread("Sample07/poster.jpg")
# Convert Image into Gray
image_gray = cv2.cvtColor(image_color, cv2.COLOR_RGB2GRAY)
# Display Image
ShowImage([image_color, image_gray], 1, 2)
In [11]:
rows,cols = image_color.shape[:2]
RotationMatrix = cv2.getRotationMatrix2D((cols/2,rows/2),90,1)
image_rotation01 = cv2.warpAffine(image_color,RotationMatrix,(cols,rows))
RotationMatrix = cv2.getRotationMatrix2D((cols/2,rows/2),45,1)
image_rotation02 = cv2.warpAffine(image_color,RotationMatrix,(cols,rows))
RotationMatrix = cv2.getRotationMatrix2D((cols/2,rows/2),-45,1)
image_rotation03 = cv2.warpAffine(image_color,RotationMatrix,(cols,rows))
ShowImage([image_color, image_rotation01], 1, 2)
ShowImage([image_rotation02, image_rotation03], 1, 2)
In [15]:
import imutils
image_rotated45 = imutils.rotate(image_color, 45)
image_rotated45_bound = imutils.rotate_bound(image_color, -45)
ShowImage([image_color, image_rotated45, image_rotated45_bound], 1, 3)
In [21]:
image_gray_blur = cv2.GaussianBlur(image_gray, (3, 3), 0)
image_edged = cv2.Canny(image_gray_blur, 50, 100)
image_edged_rotated45 = imutils.rotate(image_edged, 30)
image_edged_rotated45_bound = imutils.rotate_bound(image_edged, -30)
ShowImage([image_gray_blur, image_edged], 1, 2)
ShowImage([image_edged_rotated45, image_edged_rotated45_bound], 1, 2)
In [102]:
# Read Image
image_color = imread("Sample07/balloon.jpg")
# Convert Image into Gray
image_gray = cv2.cvtColor(image_color, cv2.COLOR_RGB2GRAY)
# Display Image
ShowImage([image_color, image_gray], 1, 2)
In [105]:
from skimage import transform as tf
# Create Afine transform
afine_tf = tf.AffineTransform(shear=0.2)
image_shear01 = tf.warp(image_color, inverse_map=afine_tf)
afine_tf = tf.AffineTransform(shear=0.5)
image_shear02 = tf.warp(image_color, inverse_map=afine_tf)
afine_tf = tf.AffineTransform(shear=-0.2)
image_shear03 = tf.warp(image_color, inverse_map=afine_tf)
ShowImage([image_color, image_shear01, image_shear02, image_shear03], 2, 2)
In [ ]:
# Affine Transformation
# In affine transformation, all parallel lines in the original image will still be parallel in the output image.
# To find the transformation matrix, we need three points from input image and their corresponding locations
# in output image. Then cv2.getAffineTransform will create a 2x3 matrix which is to be passed to cv2.warpAffine.
In [94]:
# Read Image
image_color = imread("Sample07/frozen.jpg")
image_color = ResizeImage(image_color, 700, 400)
# Convert Image into Gray
image_gray = cv2.cvtColor(image_color, cv2.COLOR_RGB2GRAY)
# Display Image
InfoImage(image_color)
ShowImage([image_color, image_gray], 1, 2)
In [95]:
rows,cols = image_color.shape[:2]
PointList_From = [[50,50],[200,50],[50,200]]
PointList_To = [[10,100],[200,50],[100,250]]
pts1 = np.float32(PointList_From)
pts2 = np.float32(PointList_To)
MappingMatrix = cv2.getAffineTransform(pts1,pts2)
image_Affine = cv2.warpAffine(image_color,MappingMatrix,(cols,rows))
print("Matrix : ")
display(MappingMatrix)
ShowImage([image_color, image_Affine], 1, 2)
In [96]:
XList = []
YList = []
for pt in PointList_From:
XList.append(pt[0])
YList.append(pt[1])
plt.figure(figsize=(10,10))
plt.imshow(image_color)
plt.scatter(XList, YList, c='y', s=200, marker='^')
plt.plot(XList,YList,'r', linewidth=5, markersize=12)
plt.show()
In [97]:
XList = []
YList = []
for pt in PointList_To:
XList.append(pt[0])
YList.append(pt[1])
plt.figure(figsize=(10,10))
plt.imshow(image_Affine)
plt.scatter(XList, YList, c='y', s=200, marker='^')
plt.plot(XList,YList,'r', linewidth=5, markersize=12)
plt.show()
In [98]:
# Read Image
image_color = imread("Sample07/monalisa.jpg")
# Convert Image into Gray
image_gray = cv2.cvtColor(image_color, cv2.COLOR_RGB2GRAY)
# Display Image
ShowImage([image_color, image_gray], 1, 2)
In [99]:
image_color = ResizeImage(image_color, 700, 400)
image_Affine = cv2.warpAffine(image_color,MappingMatrix,(cols,rows))
ShowImage([image_color, image_Affine], 1, 2)
In [ ]:
# Perspective Transformation
# For perspective transformation, you need a 3x3 transformation matrix.
# Straight lines will remain straight even after the transformation.
# To find this transformation matrix, you need 4 points on the input image and
# corresponding points on the output image. Among these 4 points, 3 of them should not be collinear.
# Then transformation matrix can be found by the function cv2.getPerspectiveTransform.
# Then apply cv2.warpPerspective with this 3x3 transformation matrix.
In [100]:
# Read Image
image_color = imread("Sample07/sudoku.jpg")
# Convert Image into Gray
image_gray = cv2.cvtColor(image_color, cv2.COLOR_RGB2GRAY)
# Display Image
InfoImage(image_color)
ShowImage([image_color, image_gray], 1, 2)
In [101]:
rows,cols,ch = image_color.shape
PointList_From = [[56,65],[368,52],[28,387],[389,390]]
PointList_To = [[0,0],[300,0],[0,300],[300,300]]
XList = []
YList = []
for pt in PointList_From:
XList.append(pt[0])
YList.append(pt[1])
plt.figure(figsize=(10,10))
plt.imshow(image_color)
plt.scatter(XList, YList, c='y', s=200, marker='^')
plt.plot(XList,YList,'r', linewidth=5, markersize=12)
plt.show()
pts1 = np.float32(PointList_From)
pts2 = np.float32(PointList_To)
MappingMatrix = cv2.getPerspectiveTransform(pts1,pts2)
image_Perspective = cv2.warpPerspective(image_color,MappingMatrix,(300,300))
ShowImage([image_color, image_Perspective], 1, 2)
XList = []
YList = []
for pt in PointList_To:
XList.append(pt[0])
YList.append(pt[1])
plt.figure(figsize=(10,10))
plt.imshow(image_Perspective)
plt.scatter(XList, YList, c='y', s=200, marker='^')
plt.plot(XList,YList,'r', linewidth=5, markersize=12)
plt.show()
